Modeling seed dormancy release and germination for predicting Avena fatua L. field emergence: A genetic algorithm approach

Autores
Blanco, Anibal Manuel; Chantre Balacca, Guillermo Ruben; Lodovichi, Mariela Victoria; Bandoni, Jose Alberto; Lopez, Ricardo L.; Vigna, Mario R.; Gigón, Ramón; Sabbatini, Mario Ricardo
Año de publicación
2014
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Avena fatua is a cosmopolite weed species which produces severe yield losses in small-grain production systems in temperate and semiarid climates. In the semiarid region of Argentina, A. fatua field emergence patterns show great year-to-year variability mainly due to the effect of highly unpredictable precipitation regimes as well as a complex seedbank dormancy behavior regulated by both, genetic and environmental factors. Previously developed models for the same agroecological system based on Non-Linear Regression techniques (NLR) and Artificial Neural Networks (ANN) were either unable to accurately predict field emergence or lacked explanatory power. The main objective ofthe present work is to develop a simple (i.e. parsimonious) model for A. fatua field emergence prediction for the semiarid region of Argentina based on the disaggregation of the dormancy release phase from the germination/pre-emergence growth processes, using easy accessible soil microclimate derived indices as input variables and observed cumulative field emergence data as output variable. The parsimony and predictive capability of the newly developed model were compared with NLR and ANN approaches developed by the same authors for the same agroecological system. Specifically, dormancy release was modeled as a logistic function of an after-ripening thermal-time index while germination/pre-emergence growth was represented by a logistic distribution of hydrothermal-time accumulation. A total of 528 input/output data pairs corresponding to 11 years of data collection were used in this study. Due to its implementation simplicity and good convergence features, a Genetic Algorithm (GA) was adopted to solve the resulting optimization problem consisting on the minimization of the Mean Square Error (MSE) between training data and experimentally obtained field emergence data. The newly developed GA based approach resulted in a significantly more parsimonious model(BIC = −1.54) compared to ANN (BIC = −1.36) and NLR (BIC = −1.32) models. Model evaluation with independent data also showed a better predictive capacity of the GA approach (RMSE = 0.07) compared to NLR (RMSE = 0.19) and ANN (RMSE = 0.11) alternatives. These outcomes suggest the potential applicability of the proposed predictive tool in weed management decision support systems design.
Fil: Blanco, Anibal Manuel. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnol.conicet - Bahia Blanca. Planta Piloto de Ingenieria Quimica (i). Grupo Vinculado Al Plapiqui - Inv. y Desarrollo En Tecnologia Quimica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Planta Piloto de Ingeniería Química (i); Argentina
Fil: Chantre Balacca, Guillermo Ruben. Universidad Nacional del Sur. Departamento de Agronomía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida(i); Argentina
Fil: Lodovichi, Mariela Victoria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida(i); Argentina. Universidad Nacional del Sur. Departamento de Agronomía; Argentina
Fil: Bandoni, Jose Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Planta Piloto de Ingeniería Química (i); Argentina. Universidad Nacional del Sur; Argentina
Fil: Lopez, Ricardo L.. Instituto Nacional de Tecnologia Agropecuaria. Centro Reg.buenos Aires. Estacion Exptal.agrop.bordenave; Argentina
Fil: Vigna, Mario R.. Instituto Nacional de Tecnologia Agropecuaria. Centro Reg.buenos Aires. Estacion Exptal.agrop.bordenave; Argentina
Fil: Gigón, Ramón . Instituto Nacional de Tecnología Agropecuaria. Chacra Experimental Integrada Barrow; Argentina
Fil: Sabbatini, Mario Ricardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida(i); Argentina. Universidad Nacional del Sur. Departamento de Agronomía; Argentina
Materia
Wild Oat
Weed Emergence Model
Dormancy Release
Germination
Genetic Algorithm
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/11432
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/11432
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Modeling seed dormancy release and germination for predicting Avena fatua L. field emergence: A genetic algorithm approachBlanco, Anibal ManuelChantre Balacca, Guillermo RubenLodovichi, Mariela VictoriaBandoni, Jose AlbertoLopez, Ricardo L.Vigna, Mario R.Gigón, Ramón Sabbatini, Mario RicardoWild OatWeed Emergence ModelDormancy ReleaseGerminationGenetic Algorithmhttps://purl.org/becyt/ford/2.11https://purl.org/becyt/ford/2Avena fatua is a cosmopolite weed species which produces severe yield losses in small-grain production systems in temperate and semiarid climates. In the semiarid region of Argentina, A. fatua field emergence patterns show great year-to-year variability mainly due to the effect of highly unpredictable precipitation regimes as well as a complex seedbank dormancy behavior regulated by both, genetic and environmental factors. Previously developed models for the same agroecological system based on Non-Linear Regression techniques (NLR) and Artificial Neural Networks (ANN) were either unable to accurately predict field emergence or lacked explanatory power. The main objective ofthe present work is to develop a simple (i.e. parsimonious) model for A. fatua field emergence prediction for the semiarid region of Argentina based on the disaggregation of the dormancy release phase from the germination/pre-emergence growth processes, using easy accessible soil microclimate derived indices as input variables and observed cumulative field emergence data as output variable. The parsimony and predictive capability of the newly developed model were compared with NLR and ANN approaches developed by the same authors for the same agroecological system. Specifically, dormancy release was modeled as a logistic function of an after-ripening thermal-time index while germination/pre-emergence growth was represented by a logistic distribution of hydrothermal-time accumulation. A total of 528 input/output data pairs corresponding to 11 years of data collection were used in this study. Due to its implementation simplicity and good convergence features, a Genetic Algorithm (GA) was adopted to solve the resulting optimization problem consisting on the minimization of the Mean Square Error (MSE) between training data and experimentally obtained field emergence data. The newly developed GA based approach resulted in a significantly more parsimonious model(BIC = −1.54) compared to ANN (BIC = −1.36) and NLR (BIC = −1.32) models. Model evaluation with independent data also showed a better predictive capacity of the GA approach (RMSE = 0.07) compared to NLR (RMSE = 0.19) and ANN (RMSE = 0.11) alternatives. These outcomes suggest the potential applicability of the proposed predictive tool in weed management decision support systems design.Fil: Blanco, Anibal Manuel. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnol.conicet - Bahia Blanca. Planta Piloto de Ingenieria Quimica (i). Grupo Vinculado Al Plapiqui - Inv. y Desarrollo En Tecnologia Quimica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Planta Piloto de Ingeniería Química (i); ArgentinaFil: Chantre Balacca, Guillermo Ruben. Universidad Nacional del Sur. Departamento de Agronomía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida(i); ArgentinaFil: Lodovichi, Mariela Victoria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida(i); Argentina. Universidad Nacional del Sur. Departamento de Agronomía; ArgentinaFil: Bandoni, Jose Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Planta Piloto de Ingeniería Química (i); Argentina. Universidad Nacional del Sur; ArgentinaFil: Lopez, Ricardo L.. Instituto Nacional de Tecnologia Agropecuaria. Centro Reg.buenos Aires. Estacion Exptal.agrop.bordenave; ArgentinaFil: Vigna, Mario R.. Instituto Nacional de Tecnologia Agropecuaria. Centro Reg.buenos Aires. Estacion Exptal.agrop.bordenave; ArgentinaFil: Gigón, Ramón . Instituto Nacional de Tecnología Agropecuaria. Chacra Experimental Integrada Barrow; ArgentinaFil: Sabbatini, Mario Ricardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida(i); Argentina. Universidad Nacional del Sur. Departamento de Agronomía; ArgentinaElsevier Science2014-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/11432Blanco, Anibal Manuel; Chantre Balacca, Guillermo Ruben; Lodovichi, Mariela Victoria; Bandoni, Jose Alberto; Lopez, Ricardo L.; et al.; Modeling seed dormancy release and germination for predicting Avena fatua L. field emergence: A genetic algorithm approach; Elsevier Science; Ecological Modelling; 272; 1-2014; 293-3000304-3800enginfo:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0304380013004808info:eu-repo/semantics/altIdentifier/doi/10.1016/j.ecolmodel.2013.10.013info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-10T13:24:26Zoai:ri.conicet.gov.ar:11336/11432instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-10 13:24:26.464CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Modeling seed dormancy release and germination for predicting Avena fatua L. field emergence: A genetic algorithm approach
title Modeling seed dormancy release and germination for predicting Avena fatua L. field emergence: A genetic algorithm approach
spellingShingle Modeling seed dormancy release and germination for predicting Avena fatua L. field emergence: A genetic algorithm approach
Blanco, Anibal Manuel
Wild Oat
Weed Emergence Model
Dormancy Release
Germination
Genetic Algorithm
title_short Modeling seed dormancy release and germination for predicting Avena fatua L. field emergence: A genetic algorithm approach
title_full Modeling seed dormancy release and germination for predicting Avena fatua L. field emergence: A genetic algorithm approach
title_fullStr Modeling seed dormancy release and germination for predicting Avena fatua L. field emergence: A genetic algorithm approach
title_full_unstemmed Modeling seed dormancy release and germination for predicting Avena fatua L. field emergence: A genetic algorithm approach
title_sort Modeling seed dormancy release and germination for predicting Avena fatua L. field emergence: A genetic algorithm approach
dc.creator.none.fl_str_mv Blanco, Anibal Manuel
Chantre Balacca, Guillermo Ruben
Lodovichi, Mariela Victoria
Bandoni, Jose Alberto
Lopez, Ricardo L.
Vigna, Mario R.
Gigón, Ramón
Sabbatini, Mario Ricardo
author Blanco, Anibal Manuel
author_facet Blanco, Anibal Manuel
Chantre Balacca, Guillermo Ruben
Lodovichi, Mariela Victoria
Bandoni, Jose Alberto
Lopez, Ricardo L.
Vigna, Mario R.
Gigón, Ramón
Sabbatini, Mario Ricardo
author_role author
author2 Chantre Balacca, Guillermo Ruben
Lodovichi, Mariela Victoria
Bandoni, Jose Alberto
Lopez, Ricardo L.
Vigna, Mario R.
Gigón, Ramón
Sabbatini, Mario Ricardo
author2_role author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Wild Oat
Weed Emergence Model
Dormancy Release
Germination
Genetic Algorithm
topic Wild Oat
Weed Emergence Model
Dormancy Release
Germination
Genetic Algorithm
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.11
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Avena fatua is a cosmopolite weed species which produces severe yield losses in small-grain production systems in temperate and semiarid climates. In the semiarid region of Argentina, A. fatua field emergence patterns show great year-to-year variability mainly due to the effect of highly unpredictable precipitation regimes as well as a complex seedbank dormancy behavior regulated by both, genetic and environmental factors. Previously developed models for the same agroecological system based on Non-Linear Regression techniques (NLR) and Artificial Neural Networks (ANN) were either unable to accurately predict field emergence or lacked explanatory power. The main objective ofthe present work is to develop a simple (i.e. parsimonious) model for A. fatua field emergence prediction for the semiarid region of Argentina based on the disaggregation of the dormancy release phase from the germination/pre-emergence growth processes, using easy accessible soil microclimate derived indices as input variables and observed cumulative field emergence data as output variable. The parsimony and predictive capability of the newly developed model were compared with NLR and ANN approaches developed by the same authors for the same agroecological system. Specifically, dormancy release was modeled as a logistic function of an after-ripening thermal-time index while germination/pre-emergence growth was represented by a logistic distribution of hydrothermal-time accumulation. A total of 528 input/output data pairs corresponding to 11 years of data collection were used in this study. Due to its implementation simplicity and good convergence features, a Genetic Algorithm (GA) was adopted to solve the resulting optimization problem consisting on the minimization of the Mean Square Error (MSE) between training data and experimentally obtained field emergence data. The newly developed GA based approach resulted in a significantly more parsimonious model(BIC = −1.54) compared to ANN (BIC = −1.36) and NLR (BIC = −1.32) models. Model evaluation with independent data also showed a better predictive capacity of the GA approach (RMSE = 0.07) compared to NLR (RMSE = 0.19) and ANN (RMSE = 0.11) alternatives. These outcomes suggest the potential applicability of the proposed predictive tool in weed management decision support systems design.
Fil: Blanco, Anibal Manuel. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnol.conicet - Bahia Blanca. Planta Piloto de Ingenieria Quimica (i). Grupo Vinculado Al Plapiqui - Inv. y Desarrollo En Tecnologia Quimica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Planta Piloto de Ingeniería Química (i); Argentina
Fil: Chantre Balacca, Guillermo Ruben. Universidad Nacional del Sur. Departamento de Agronomía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida(i); Argentina
Fil: Lodovichi, Mariela Victoria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida(i); Argentina. Universidad Nacional del Sur. Departamento de Agronomía; Argentina
Fil: Bandoni, Jose Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Planta Piloto de Ingeniería Química (i); Argentina. Universidad Nacional del Sur; Argentina
Fil: Lopez, Ricardo L.. Instituto Nacional de Tecnologia Agropecuaria. Centro Reg.buenos Aires. Estacion Exptal.agrop.bordenave; Argentina
Fil: Vigna, Mario R.. Instituto Nacional de Tecnologia Agropecuaria. Centro Reg.buenos Aires. Estacion Exptal.agrop.bordenave; Argentina
Fil: Gigón, Ramón . Instituto Nacional de Tecnología Agropecuaria. Chacra Experimental Integrada Barrow; Argentina
Fil: Sabbatini, Mario Ricardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida(i); Argentina. Universidad Nacional del Sur. Departamento de Agronomía; Argentina
description Avena fatua is a cosmopolite weed species which produces severe yield losses in small-grain production systems in temperate and semiarid climates. In the semiarid region of Argentina, A. fatua field emergence patterns show great year-to-year variability mainly due to the effect of highly unpredictable precipitation regimes as well as a complex seedbank dormancy behavior regulated by both, genetic and environmental factors. Previously developed models for the same agroecological system based on Non-Linear Regression techniques (NLR) and Artificial Neural Networks (ANN) were either unable to accurately predict field emergence or lacked explanatory power. The main objective ofthe present work is to develop a simple (i.e. parsimonious) model for A. fatua field emergence prediction for the semiarid region of Argentina based on the disaggregation of the dormancy release phase from the germination/pre-emergence growth processes, using easy accessible soil microclimate derived indices as input variables and observed cumulative field emergence data as output variable. The parsimony and predictive capability of the newly developed model were compared with NLR and ANN approaches developed by the same authors for the same agroecological system. Specifically, dormancy release was modeled as a logistic function of an after-ripening thermal-time index while germination/pre-emergence growth was represented by a logistic distribution of hydrothermal-time accumulation. A total of 528 input/output data pairs corresponding to 11 years of data collection were used in this study. Due to its implementation simplicity and good convergence features, a Genetic Algorithm (GA) was adopted to solve the resulting optimization problem consisting on the minimization of the Mean Square Error (MSE) between training data and experimentally obtained field emergence data. The newly developed GA based approach resulted in a significantly more parsimonious model(BIC = −1.54) compared to ANN (BIC = −1.36) and NLR (BIC = −1.32) models. Model evaluation with independent data also showed a better predictive capacity of the GA approach (RMSE = 0.07) compared to NLR (RMSE = 0.19) and ANN (RMSE = 0.11) alternatives. These outcomes suggest the potential applicability of the proposed predictive tool in weed management decision support systems design.
publishDate 2014
dc.date.none.fl_str_mv 2014-01
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/11432
Blanco, Anibal Manuel; Chantre Balacca, Guillermo Ruben; Lodovichi, Mariela Victoria; Bandoni, Jose Alberto; Lopez, Ricardo L.; et al.; Modeling seed dormancy release and germination for predicting Avena fatua L. field emergence: A genetic algorithm approach; Elsevier Science; Ecological Modelling; 272; 1-2014; 293-300
0304-3800
url http://hdl.handle.net/11336/11432
identifier_str_mv Blanco, Anibal Manuel; Chantre Balacca, Guillermo Ruben; Lodovichi, Mariela Victoria; Bandoni, Jose Alberto; Lopez, Ricardo L.; et al.; Modeling seed dormancy release and germination for predicting Avena fatua L. field emergence: A genetic algorithm approach; Elsevier Science; Ecological Modelling; 272; 1-2014; 293-300
0304-3800
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0304380013004808
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.ecolmodel.2013.10.013
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
application/pdf
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application/pdf
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dc.publisher.none.fl_str_mv Elsevier Science
publisher.none.fl_str_mv Elsevier Science
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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